

'''simple docstring'''





import os
from glob import glob

import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn

from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil


class  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_ = "cpu"   ,			SCREAMING_SNAKE_CASE_ = "openai/clip-vit-large-patch14"		)  ->     None:






							'''simple docstring'''



							lowerCamelCase_  				=      device
							lowerCamelCase_  				=      CLIPTokenizerFast.from_pretrained(lowerCAmelCase__		)
							lowerCamelCase_  				=      [0.48_145_466, 0.4_578_275, 0.40_821_073]
							lowerCamelCase_  				=      [0.26_862_954, 0.26_130_258, 0.27_577_711]
							lowerCamelCase_  				=      torchvision.transforms.Normalize(self.image_mean   ,			self.image_std		)
							lowerCamelCase_  				=      torchvision.transforms.Resize(224		)
							lowerCamelCase_  				=      torchvision.transforms.CenterCrop(224		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.resize(lowerCAmelCase__		)
							lowerCamelCase_  				=      self.center_crop(lowerCAmelCase__		)
							lowerCamelCase_  				=      self.normalize(lowerCAmelCase__		)
							return images






		def __call__(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			**SCREAMING_SNAKE_CASE_		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.tokenizer(text=lowerCAmelCase__   ,			**lowerCAmelCase__		)
							lowerCamelCase_  				=      self.preprocess_img(lowerCAmelCase__		)
							lowerCamelCase_  				=      {key: value.to(self.device		) for (key, value) in encoding.items()}
							return encoding


class  UpperCAmelCase (							nn.Module    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=0.01   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_="image"   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=False   ,			)  ->     None:






							'''simple docstring'''



							super().__init__()
							lowerCamelCase_  				=      None
							lowerCamelCase_  				=      device if device else get_device()
							if vqgan:
												lowerCamelCase_  				=      vqgan
							else:
												lowerCamelCase_  				=      load_vqgan(self.device   ,			conf_path=lowerCAmelCase__   ,			ckpt_path=lowerCAmelCase__		)
							self.vqgan.eval()
							if clip:
												lowerCamelCase_  				=      clip
							else:
												lowerCamelCase_  				=      CLIPModel.from_pretrained('openai/clip-vit-base-patch32'		)
							self.clip.to(self.device		)
							lowerCamelCase_  				=      ProcessorGradientFlow(device=self.device		)

							lowerCamelCase_  				=      iterations
							lowerCamelCase_  				=      lr
							lowerCamelCase_  				=      log
							lowerCamelCase_  				=      make_grid
							lowerCamelCase_  				=      return_val
							lowerCamelCase_  				=      quantize
							lowerCamelCase_  				=      self.vqgan.decoder.z_shape
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=5   ,			SCREAMING_SNAKE_CASE_=True		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      []
							if output_path is None:
												lowerCamelCase_  				=      './animation.gif'
							if input_path is None:
												lowerCamelCase_  				=      self.save_path
							lowerCamelCase_  				=      sorted(glob(input_path + '/*'		)		)
							if not len(lowerCAmelCase__		):
												raise ValueError(
												    'No images found in save path, aborting (did you pass save_intermediate=True to the generate'
												    ' function?)'		)
							if len(lowerCAmelCase__		) == 1:
												print('Only one image found in save path, (did you pass save_intermediate=True to the generate function?)'		)
							lowerCamelCase_  				=      total_duration / len(lowerCAmelCase__		)
							lowerCamelCase_  				=      [frame_duration] * len(lowerCAmelCase__		)
							if extend_frames:
												lowerCamelCase_  				=      1.5
												lowerCamelCase_  				=      3
							for file_name in paths:
												if file_name.endswith('.png'		):
																	images.append(imageio.imread(lowerCAmelCase__		)		)
							imageio.mimsave(lowerCAmelCase__   ,			lowerCAmelCase__   ,			duration=lowerCAmelCase__		)
							print(f'''gif saved to {output_path}'''		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None		)  ->     Optional[int]:






							'''simple docstring'''



							if not (path or img):
												raise ValueError('Input either path or tensor'		)
							if img is not None:
												raise NotImplementedError
							lowerCamelCase_  				=      preprocess(Image.open(lowerCAmelCase__		)   ,			target_image_size=256		).to(self.device		)
							lowerCamelCase_  				=      preprocess_vqgan(lowerCAmelCase__		)
							lowerCamelCase_				,*lowerCamelCase_  				=      self.vqgan.encode(lowerCAmelCase__		)
							return z
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.latent.detach().requires_grad_()
							lowerCamelCase_  				=      base_latent + transform_vector
							if self.quantize:
												lowerCamelCase_				,*lowerCamelCase_  				=      self.vqgan.quantize(lowerCAmelCase__		)
							else:
												lowerCamelCase_  				=      trans_latent
							return self.vqgan.decode(lowerCAmelCase__		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=None		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.clip_preprocessor(text=lowerCAmelCase__   ,			images=lowerCAmelCase__   ,			return_tensors='pt'   ,			padding=lowerCAmelCase__		)
							lowerCamelCase_  				=      self.clip(**lowerCAmelCase__		)
							lowerCamelCase_  				=      clip_outputs.logits_per_image
							if weights is not None:
												lowerCamelCase_  				=      similarity_logits * weights
							return similarity_logits.sum()
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self._get_clip_similarity(pos_prompts['prompts']   ,			lowerCAmelCase__   ,			weights=(1 / pos_prompts['weights'])		)
							if neg_prompts:
												lowerCamelCase_  				=      self._get_clip_similarity(neg_prompts['prompts']   ,			lowerCAmelCase__   ,			weights=neg_prompts['weights']		)
							else:
												lowerCamelCase_  				=      torch.tensor([1]   ,			device=self.device		)
							lowerCamelCase_  				=      -torch.log(lowerCAmelCase__		) + torch.log(lowerCAmelCase__		)
							return loss
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      torch.randn_like(self.latent   ,			requires_grad=lowerCAmelCase__   ,			device=self.device		)
							lowerCamelCase_  				=      torch.optim.Adam([vector]   ,			lr=self.lr		)

							for i in range(self.iterations		):
												optim.zero_grad()
												lowerCamelCase_  				=      self._add_vector(lowerCAmelCase__		)
												lowerCamelCase_  				=      loop_post_process(lowerCAmelCase__		)
												lowerCamelCase_  				=      self._get_CLIP_loss(lowerCAmelCase__   ,			lowerCAmelCase__   ,			lowerCAmelCase__		)
												print('CLIP loss'   ,			lowerCAmelCase__		)
												if self.log:
																	wandb.log({'CLIP Loss': clip_loss}		)
												clip_loss.backward(retain_graph=lowerCAmelCase__		)
												optim.step()
												if self.return_val == "image":
																	yield custom_to_pil(transformed_img[0]		)
												else:
																	yield vector
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Dict:






							'''simple docstring'''



							wandb.init(reinit=lowerCAmelCase__   ,			project='face-editor'		)
							wandb.config.update({'Positive Prompts': positive_prompts}		)
							wandb.config.update({'Negative Prompts': negative_prompts}		)
							wandb.config.update({'lr': self.lr, 'iterations': self.iterations}		)
							if image_path:
												lowerCamelCase_  				=      Image.open(lowerCAmelCase__		)
												lowerCamelCase_  				=      image.resize((256, 256)		)
												wandb.log('Original Image'   ,			wandb.Image(lowerCAmelCase__		)		)
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							if not prompts:
												return []
							lowerCamelCase_  				=      []
							lowerCamelCase_  				=      []
							if isinstance(lowerCAmelCase__   ,			lowerCAmelCase__		):
												lowerCamelCase_  				=      [prompt.strip() for prompt in prompts.split('|'		)]
							for prompt in prompts:
												if isinstance(lowerCAmelCase__   ,			(tuple, list)		):
																	lowerCamelCase_  				=      prompt[0]
																	lowerCamelCase_  				=      float(prompt[1]		)
												elif ":" in prompt:
																	lowerCamelCase_				,lowerCamelCase_  				=      prompt.split(':'		)
																	lowerCamelCase_  				=      float(lowerCAmelCase__		)
												else:
																	lowerCamelCase_  				=      prompt
																	lowerCamelCase_  				=      1.0
												processed_prompts.append(lowerCAmelCase__		)
												weights.append(lowerCAmelCase__		)
							return {
							    "prompts": processed_prompts,
							    "weights": torch.tensor(lowerCAmelCase__   ,			device=self.device		),
							}






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=None   ,			)  ->     int:






							'''simple docstring'''



							if image_path:
												lowerCamelCase_  				=      self._get_latent(lowerCAmelCase__		)
							else:
												lowerCamelCase_  				=      torch.randn(self.latent_dim   ,			device=self.device		)
							if self.log:
												self._init_logging(lowerCAmelCase__   ,			lowerCAmelCase__   ,			lowerCAmelCase__		)

							assert pos_prompts, "You must provide at least one positive prompt."
							lowerCamelCase_  				=      self.process_prompts(lowerCAmelCase__		)
							lowerCamelCase_  				=      self.process_prompts(lowerCAmelCase__		)
							if save_final and save_path is None:
												lowerCamelCase_  				=      os.path.join('./outputs/'   ,			'_'.join(pos_prompts['prompts']		)		)
							if not os.path.exists(lowerCAmelCase__		):
												os.makedirs(lowerCAmelCase__		)
							else:
												lowerCamelCase_  				=      save_path + '_' + get_timestamp()
												os.makedirs(lowerCAmelCase__		)
							lowerCamelCase_  				=      save_path

							lowerCamelCase_  				=      self.vqgan.decode(self.latent		)[0]
							if show_intermediate:
												print('Original Image'		)
												show_pil(custom_to_pil(lowerCAmelCase__		)		)

							lowerCamelCase_  				=      loop_post_process(lowerCAmelCase__		)
							for iter, transformed_img in enumerate(self._optimize_CLIP(lowerCAmelCase__   ,			lowerCAmelCase__   ,			lowerCAmelCase__		)		):
												if show_intermediate:
																	show_pil(lowerCAmelCase__		)
												if save_intermediate:
																	transformed_img.save(os.path.join(self.save_path   ,			f'''iter_{iter:03d}.png'''		)		)
												if self.log:
																	wandb.log({'Image': wandb.Image(lowerCAmelCase__		)}		)
							if show_final:
												show_pil(lowerCAmelCase__		)
							if save_final:
												transformed_img.save(os.path.join(self.save_path   ,			f'''iter_{iter:03d}_final.png'''		)		)




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__a    :	int         =    logging.get_logger(__name__)

__a    :	Optional[int]         =    {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			__a    :					Any     					=		'''pegasus'''
			__a    :					Optional[Any]     					=		['''past_key_values''']
			__a    :					Tuple     					=		{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





			def __init__(  self ,  lowerCAmelCase__=5_02_65 ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=12 ,  lowerCAmelCase__=40_96 ,  lowerCAmelCase__=16 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=True ,  lowerCAmelCase__=True ,  lowerCAmelCase__="gelu" ,  lowerCAmelCase__=10_24 ,  lowerCAmelCase__=0.1 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.0 ,  lowerCAmelCase__=0.02 ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=False ,  lowerCAmelCase__=0 ,  lowerCAmelCase__=1 ,  lowerCAmelCase__=1 ,  **lowerCAmelCase__ ,  )	->   Dict:

							'''simple docstring'''


							__lowercase					     =				vocab_size
							__lowercase					     =				max_position_embeddings
							__lowercase					     =				d_model
							__lowercase					     =				encoder_ffn_dim
							__lowercase					     =				encoder_layers
							__lowercase					     =				encoder_attention_heads
							__lowercase					     =				decoder_ffn_dim
							__lowercase					     =				decoder_layers
							__lowercase					     =				decoder_attention_heads
							__lowercase					     =				dropout
							__lowercase					     =				attention_dropout
							__lowercase					     =				activation_dropout
							__lowercase					     =				activation_function
							__lowercase					     =				init_std
							__lowercase					     =				encoder_layerdrop
							__lowercase					     =				decoder_layerdrop
							__lowercase					     =				use_cache
							__lowercase					     =				encoder_layers
							__lowercase					     =				scale_embedding  # scale factor will be sqrt(d_model) if True
							super().__init__(
							    pad_token_id=lowerCAmelCase__ ,  eos_token_id=lowerCAmelCase__ ,  is_encoder_decoder=lowerCAmelCase__ ,  decoder_start_token_id=lowerCAmelCase__ ,  forced_eos_token_id=lowerCAmelCase__ ,  **lowerCAmelCase__ ,  )





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.encoder_attention_heads





			@property
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							return self.d_model